types the analysis of semantic differential attitude data

469

Two Types of Factors in the Analysis ofSemantic Differential Attitude Data

Cathleen Kubiniec Mayerberg and Andrew G. BeanTemple University

Evidence is presented for the existence of twotypes of factors when semantic differential data arefactor analyzed by treating each concept-scale com-bination as a variable: (1) factors defined by scaleswithin given concepts and (2) factors defined byscales across concepts. These two types of factorswere found for each of two independent data sets.The findings suggest changes in the procedures in-vestigators typically use to select scales, to analyzetheir three-dimensional array, and to obtain at-titude scores.

Originally developed as a psycholinguistictool to assess the dimensionality of connotativemeaning, the semantic differential technique(Osgood, Suci, & Tannenbaum, 1957) has subse-quently been used in a wide variety of situationsfor a wide variety of purposes. In its original use,interest was primarily in assessing the dimen-sionality of connotative meaning across a widerange of heterogeneous concepts. In one of itssubsequent applications, however, interest hasbeen in assessing the connotative meaning of ahomogeneous domain of concepts, usually forthe specific purpose of assessing attitude towardthat domain. Although the intentions of the psy-cholinguist and the attitude researcher differ,the latter group has nevertheless tended to em-

APPLIED PSYCHOLOGICAL MEASUREMENTVol. 2, No. 4 Fall 1978 pp. 469-480@ Copyright 1978 West Publishing Co.

ploy procedures for selecting scales, for combin-ing scales to define scores, and for analyzing thedata that are similar to the procedures originallyused. Use of the original procedures when meas-uring attitude may obscure important informa-tion and, more importantly, limit the validity ofthe technique as a measure of attitude. Thisstudy enumerates some of these misapplicationsand presents empirical data to support its argu-ments.

Across numerous factor analyses of psycholin-guistic semantic differential data, investigatorshave consistently found evidence for theexistence of three major dimensions of connota-tive meaning-Evaluation, Activity, and

Potency. That is, scales typically load on one ofthese three meaning dimensions, and these di-mensions in combination typically account forthe major proportion of the explained variance.

In attitude studies, however, there is extensiveevidence (e.g., Gulliksen, 1958; Osgood et al.,1957; Heise, 1969) to indicate that the loading ofgiven scales on given meaning dimensions is afunction of the particular class of concepts em-ployed. The label concept-scale interaction hasbeen used to refer to this situation, where &dquo;the

meanings of scales and their relations to otherscales vary considerably with the concept beingjudged&dquo; (Osgood et al., 1957, p. 187). Eventhough there are several possible interpretationsof concept-scale interaction, depending upon

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470

whether it is a true instance of concept-scale in-teraction or whether it is due to any one of

several methodological artifacts (Heise, 1969),its empirical existence has been supported ex-tensively. Its occurrence indicates that investiga-tors should not assume the dimensionality ofscales on the basis of previous research based onheterogeneous concepts or previous research

based on a class of concepts different from theone currently being studied. Further, it indicatesthey should not sum across scales presumablyrepresenting a given meaning dimension in or-der to obtain a more reliable score until theyhave determined that these scales do in fact

represent the same meaning dimension in theirspecific application, i.e., with respect to their

specific concepts.In addition to providing further empirical evi-

dence of concept-scale interaction, a factor

analysis of a semantic differential developed byKubiniec and Farr (1971) reported evidence of apattern of factors in which scales representingthe same meaning dimension tended to clustertogether within each of several concepts as wellas loading on independent factors across con-cepts. That is, two interpretable categories offactors appeared in their data: (1) a categoryconsisting of factors defined by high loadings onmost or all scales within each of several specificconcepts, and (2) a category consisting of factorsdefined by high loadings of a small number ofscales across all concepts within a domain.

This pattern was discovered as a result of

analyzing their semantic differential in a man-ner different from that usually employed. Theratings obtained from a semantic differentialcreate a three-dimensional array consisting ofsubjects x concepts x scales. This array is typi-cally reduced to two dimensions by summing oraveraging responses over one of these three di-mensions, by &dquo;stringing out&dquo; concepts and sub-jects or scales and subjects, or by analyzing oneconcept at a time. Kubiniec and Farr, on theother hand, &dquo;strung out&dquo; concepts and scales.That is, each concept-scale combination servedas a variable, with subjects as the second dimen-

sion of the data box. The existence of the two

patterns they obtained would not have been evi-dent if responses had been summed or averagedacross concepts, or if concepts and subjects orscales and subjects had been &dquo;strung out.&dquo; Thepattern obtained suggests that important in-formation may be obscured when individual

concepts are not represented in the analysis ofsemantic differential data, when the instrumentis being used to assess the connotative meaningof a specific class of concepts rather than to as-sess the dimensionality of connotative meaning.

Specifically, the pattern suggests that investi-gators should not automatically assume thatsubjects will respond similarly to a series of con-cepts simply because the concepts represent thesame domain. There could be differences, andfailure to ascertain these differences could resultin invalid conclusions. Furthermore, such differ-ences from concept to concept may be of interestin and of themselves. They may, for example,suggest that attitude toward the concept domainis better conceptualized as multidimensionalthan as unidimensional.The purpose of this study was to use the same

&dquo;stringing out&dquo; method as that used by Ku-biniec and Farr with two independent sets ofsemantic differential data (different popula-tions, different scales, and different concept do-mains) to determine whether the same two cate-gories of factors they found, i.e., within-conceptfactors and across-concept factors, would bereplicated. The first analysis was similar to theKubiniec and Farr study in that one homogene-ous domain of concepts was used. The secondanalysis, however, included two homogeneousdomains of concepts, providing a more stringenttest of the validity of the across-concept factors.In this second analysis, the across-conceptfactors would have to be defined only across theconcepts within each of the two domains inorder to be meaningful; the existence of factorsdefined by a small number of scales across bothdomains of concepts would suggest a method-ological artifact, i.e., a response style, ratherthan a meaningful factor pattern.



471

Analysis I1

Subjects were students enrolled in one of threegraduate level courses in a college of education.The total sample size was 311 (139 men; 172women).

Six concepts, all representing the quantitativedomain of mental abilities, were used. They in-cluded : ALGEBRA, NUMBERS, STATIS-

TICS, MATHEMATICS, CALCULATIONS,and FORMULAS.

Fourteen bipolar adjective scales were used.They were: enjoyable-unenjoyable, attractive-re-pellant, pleasant-unpleasant, valuable-worth-

less, simple-complex, lucid-obscure, interesting-boring, clear-hazy, good-bad, meaningful-meaningless, intelligible-unintelligible, easy-dif-ficult, important-unimportant, and useful-use-less.2

Unlike the Kubiniec and Farr study, all of thescales included were selected on the basis oftheir meaningfulness to the specific conceptsbeing rated, rather than because they repre-sented one of Osgood’s three major meaning di-mensions. More specifically, since the interestwas in measuring attitude, all of the scales in-cluded were presumed to represent the Evalua-tive meaning dimension.The concept-scale combination variables (6

concepts x 14 scales = 84 variables) were

analyzed using principal factor analysis. Since ahomogeneous domain of concepts was used, cor-relations among the factors were expected.Hence, oblique rotation was used. A simpleloadings rotational procedure (Jennrich &

Sampson, 1966) with y = 0 was used. The num-ber of factors to be rotated was initially esti-mated by Cattell’s scree test (Cattell, 1966, p.

’ These data were originally obtained to study the nature ofthe dimensionality of attitude toward quantitative concepts.See Mayerberg and Bean (1974) for further informationabout the instrument. Table 1 is taken from pages 316-317

of this study.

2 Henceforth, only the positive ends of the scales will belisted.

206) as 10. Trial rotations from 8 to 12 factorsindicated that a 9-factor solution was most satis-

factory ; evidence of factor fission (Cattell, 1966,p. 209) was observed with 10 or more factors.

Results

&dquo;Stringing out&dquo; analysis. Table 1 presentsthe rotated factor loadings >.30. Since all of thescales used were Evaluative in nature, it mighthave been anticipated that all of the scaleswould have clustered together in one meaningdimension, i.e., Evaluative. However, as indica-ted in Table 1, while all of the scales did tend tocluster together within concepts (Factors I, II,III, and IV), they also broke up into subcate-gories, defining factors across concepts (FactorsV, VI, VII, and IX). For example, the scales Lu-cid, Clear, and Intelligible split off from theother Evaluative scales to define Factor V; simil-arly, for the scales Simple and Easy (Factor VII)and the scales Valuable, Meaningful, Im-

portant, and Useful (Factor VI). Finally, it is ofinterest to note that the primary Evaluativescale, the Good scale, itself defined a factor(Factor IX) rather than clustering with the otherEvaluative scales.

Thus, even with a presumably homogeneousset of scales, all representing the same connota-tive meaning dimension as well as a homogene-ous set of concepts, there is evidence of concept-scale interaction. In this case, however, the na-ture of the concept-scale interaction differedfrom that which is typically found. While noscale changed meaning dimension (since all ofthe scales were Evaluative scales), the Evaluativescales split up, creating subcategories of Evalu-ation. This finding is consistent with that ofKomorita and Bass (1967), who found threefactors for each of two concepts using onlyEvaluative scales.

Thus, similar to the Kubiniec and Farr find-

ings, there is clear evidence of the existence oftwo patterns of factors. Moreover, the factorsobtained appear to be meaningful. The factorsdefined by loadings of most or all scales within a



472

Table 1

Simple Loadings Rotated Factor Pattern Matrix------ ------------ -



473

Table 1 (continued)Simple Loadings Rotated Factor Pattern Matrix

Note: All loadings > .30 are listed.. To indicate structure more clearly, some loadingsbetween .20 and .29 are also listed, in parentheses. The entire matrix of loadings isavailable from the authors. For ease of interpretation, the factors have been reordered.



474

given concept are not surprising and are readilyinterpretable; they reflect a global or general-ized attitude toward a given concept. Similarly,the factors defined by loadings of a small num-ber of scales across concepts are interpretable:They reflect somewhat more specific dimensionsof attitude toward the domain as a whole. Thesubsets of scales clustering together (e.g., simpleand easy; lucid, clear, and intelligible; valuable,important, meaningful, and useful) are intuitive-ly pleasing. Subsets of variables such as simpleand valuable, or easy and useful, on the otherhand, would not have been as interpretable.

Thus, using a different population, a differentclass of concepts, a different set of scales (repre-senting only one rather than three connotativemeaning dimensions), and a different data re-duction technique, the factor pattern obtainedin Analysis I was similar to that reported by Ku-biniec and Farr. In both cases, the factor patternobtained was readily interpretable (see Farr &

Kubiniec, 1972; Mayerberg & Bean, 1974).Three-mode analysis. An alternative to re-

ducing semantic differential data from three di-mensions to two dimensions is employing three-mode factor analysis (Tucker, 1966). Actually,the factor analysis of concept-scale variablesused in Analysis I is the first step in the three-mode method. A three-mode factor analysis pro-duces the following additional information: (1) afactor pattern matrix for concepts, derived fromthe average concept by concept correlation ma-trix ; (2) a factor pattern matrix for scales, de-rived from the average scale by scale correlationmatrix; and (3) a core matrix showing the inter-relationships among the three factor analyticsolutions.

Subsequent to the completion of Analysis I,additional subjects were measured using thesame semantic differential instrument, yieldingdata for a total of 667 subjects. This data set wasthen analyzed using three-mode procedures, sothat a comparison could be made of the two-di-mensional approach and the three-dimensionalapproach.3 3

3 A full presentation of the three-mode factor analysis hasbeen prepared for publication as a separate paper.

The three-mode analysis began with a factoranalysis of concept-scale variables. A nine-factorsolution was obtained, which was essentially thesame as that described previously. As indicatedabove, a three-mode analysis provides three ma-trices in addition to the factor matrix of concept-scale variables. The factor pattern matrix forconcepts consisted of one general factor, with allconcepts loading highly on it. This result was ex-pected, since all six concepts were chosen torepresent a single homogeneous concept do-main. The factor pattern matrix for scales con-tained three factors, all of which were easily in-terpretable : IMPORTANT, PLEASANT, andEASY. (Each factor was named for its highestloading.) The core matrix showed the interrela-tionships among the three-factor analytic solu-tions. Since there was only one concept factor,the core matrix was two-dimensional andshowed how the nine concept-scale factors re-lated to the three scale factors. It was clear thatthe single most detailed piece of information ob-tained from the three-mode procedure was theconcept-scale factor solution. As indicated

earlier, the concept-scale factor solution is readi-ly obtainable from a conventional two-mode fac-tor analysis such as the one presented in Table 1.

Analysis ll4

As indicated earlier, stronger evidence of the

meaningfulness of the two types of factors wouldbe forthcoming if an analysis of semantic dif-ferential data representing two different conceptdomains resulted in the presence of factors de-fined by loadings of the scales across conceptswithin each of the two concept classes and theabsence of factors defined by scales across con-

4 The data analyzed in Analysis II originated from the sameinstrument as that used in the Kubiniec and Farr study. The

analysis presented here, however, differs from theirs in threeways: (1) it includes two concept domains instead of one; (2)it was analyzed using principal factor analysis with obliquerotation rather than principal components analysis with or-

thogonal rotation; and (3) it included both men and women.See Kubiniec and Farr (1971) for further information aboutthe instrument.



475

cept classes. That is, responses to concepts with-in a given concept domain should be more simi-lar than responses to concepts across conceptsdomains. Hence, whereas factors defined byloadings within each of the two domains wouldbe reasonable, factors defined by loadings acrossthe two domains would not. Analysis II, consist-ing of two domains of concepts, allows us to de-termine whether this is, in fact, the case.

Subjects were freshmen in a state university.The total sample size was 584 (324 men; 260women). The concepts rated by the subjects re-lated to one of two concept domains. The firstdomain, the self-concept domain, consisted ofthe following three concepts: MY PAST, MYREAL SELF, and MY IDEAL SELF. Thesecond domain, the academic domain, consistedof the following three concepts: STUDYING,LEARNING, and READING.

Fifteen bipolar adjective scales, presumablyreflecting the three major dimensions of con-notative meaning, were used. The Evaluativescales were good-bad, useful-useless, important-unimportant, interesting-boring, and enjoyable-unenjoyable ; the Potency scales were strong-weak, serious-humorous, masculine-feminine,severe-lenient, and rugged-delicate; the Activityscales were active-passive, excitable-calm, com-plex-simple, tense-relaxed, and energetic-lethar-gic.As was the case with Analysis I, the concept-

scale variables (6 concepts x 1 S scales = 90 vari-ables) were analyzed using principal factor

analysis with oblique rotation. A simple loadingsrotational procedure (Jennrich & Sampson,1966) with y = 0, was used. The number of fac-tors to be rotated was initially estimated by Cat-tell’s scree test (Cattell, 1966, p. 206) as 15. Trialrotations from 12 to 16 factors indicated that a14-factor solution provided the most inter-

pretable structure.

5 In that the two-category factor pattern was not as evidentin Analysis II as it was in Analysis I, and the primary interestin Analysis II was in whether there would be any factors de-fined by scales across the two concept domains, the factors inAnalysis II were reordered in Table 2 to maximize ease of in-terpretation with respect to the two concept domains, rather

Results

Table 2 presents the rotated factor loadings >.30.5 The most important aspect of Analysis II,given the purpose of this study, is the fact that,with one exception (Factor XIV),6 no factorswere defined by scales loading across conceptsacross the two concept domains. Such factorswould not be expected psychologically; that is,there is no reason to expect subjects’ responsesto the academic concepts to be similar to theirresponses to self-concepts. As indicated in Table2, however, half of the factors were defined byloadings on concepts in the Academic domain,while half were defined by loadings on conceptsin the Self domain, lending credence to the argu-ment that the factors obtained when conceptsand scales are &dquo;strung out&dquo; to create variablesare meaningful factors rather than methodologi-cal artifacts.

As in the Kubiniec and Farr study and inAnalysis I, there was evidence of concept-scaleinteraction. That is, the five scales selected to

represent each of the three meaning dimen-sions-Evaluative, Potency, and Activity-diednot cluster together to form three factors.

Rather, the scales separated into several smallclusters. Factor I, for example, was defined byonly two Evaluative scales, Factor II by two Ac-tivity scales, Factor III by one Potency scale,Factor VIII by two Potency scales, Factor XIVby two Activity scales, and so forth. Further,scales representing all three meaning dimen-sions combined to define within-concept factors(e.g., Factors VII and XIII).

Evidence for the existence of two types of fac-tors, i.e., within-concept factors and across-con-cept factors, was less clear than was the case inAnalysis I. While there were several across-con-cept factors, i.e., factors defined by one scale ora small subset of scales clustering together

than with respect to the two categories of factors as was donein Analysis I (Table 1).6 Factor XIV appears to be an excitable/tense factor; thesetwo scales loaded on this factor for all three of the Self do-

main concepts as well as for both the STUDYING and

LEARNING concepts in the Academic domain.



476

Table 2

-

Simple Loadings Rotated Factor Pattern Matrix

(continued on the next page)



477

Table 2 (continued)Simple Loadings Rotated Factor Pattern Ilatrix

Note: All loadings ~ .30 are listed. To indicate structure more clearly, some loadingsbetween .20 and .29 are also listed, in parentheses. The entire matrix of loadings isavailable from the authors. For ease of interpretation, the factors have been reordered.



478

across concepts within one of the two (Self andAcademic) domains (e.g., Factors I, III, VIII,and XI), there were few within-concept factors.The only factor in the Academic domain thatsuggested a within-concept factor was FactorVII. Contrary to Analysis I, however, Factor VIIwas not a global or general factor for the specificconcept READING; only 7 of the 15 scalesloaded > .30 on this factor. The only factors inthe Self domain that suggested within-conceptfactors were Factor XIII, reflecting a somewhatglobal attitude toward the concept MY IDEALSELF (9 of the 15 scales loaded >,.30 on this fac-

tor), and Factor XII, reflecting a somewhatglobal attitude toward the concepts MY PASTand MY REAL SELF (for each of the two con-cepts, 8 of the 15 scales loaded > .30 on this fac-

tor).Presumably, the lack of clear concept factors

is due to the fact that in Analysis II, scales repre-senting each of three meaning dimensions ratherthan only the Evaluative meaning dimensionwere used. One would expect a tendency for allof the scales to cluster together if they all repre-sent the same meaning dimension. On the otherhand, when scales are selected to represent eachof three meaning dimensions, one would not ex-pect all of the scales to cluster together.As was the case with Analysis I, the factors

obtained in Analysis II appeared to be meaning-ful. Across concepts within a domain, the samescales rather consistently loaded on a given fac-tor. Further, the scales that did cluster togetherreflected similar terms; with respect to the con-notative meaning dimensions, they typicallyrepresented the same meaning dimension.Another reflection of the meaningfulness of

the factor pattern obtained is evident in a com-

parison of the factors defined by the two do-mains. For the self-concept domain, two globalEvaluative factors were obtained: one for thecombination of the concepts MY PAST and MYREAL SELF (Factor XII) and one for the

concept MY IDEAL SELF (Factor XIII). Al-though it is reasonable to expect that people’sperception of their past would be similar to their

perception of their &dquo;real&dquo; self, it is not reason-able to expect that people’s perceptions of theirpast or their real self would be similar to theirperception of their &dquo;ideal&dquo; self.

In the Academic concept domain, on theother hand, evidence for the existence of globalEvaluative factors was minimal. This is not sur-

prising ; as Osgood stated (Osgood et al., 1957,p. 187), &dquo;... the more evaluative (emotionallyloaded?) the concept being judged, the more themeaning of all scales shifts toward evaluativeconnotations.&dquo; Surely &dquo;self&dquo; concepts are moreemotionally loaded than Academic concepts.

There is one final comparison of interest inAnalysis II. The scales that combined to definefactors in the Self domain were by and large dif-ferent from the scales that combined to definefactors for the Academic domain. For example,while there was a serious/severe factor (FactorVIII) and a simple factor (Factor XI) for the Selfdomain, no similar factors existed for the Aca-demic domain; while there was an interest-

ing/enjoyable factor (Factor I) and a masculinefactor (Factor III) for the Academic domain, nocounterpart existed for the Self domain. (Themasculine scale clustered with other Potencyscales in the Self domain, defining Factor X). Ineffect, this is yet another indication of the exist-ence of concept-scale interaction. Again, the evi-dence clearly indicates that the function servedby a given scale (i.e., the connotative meaningdimension it reflects) varies as a function of theparticular concept or concepts being rated.

Conclusions and Implications

Across different sets of scales, different con-cept classes, and different populations, similarpatterns of results were obtained; namely, fac-tors defined by scales within concepts and fac-tors defined by scales across concepts. Further,there was evidence of concept-scale interaction.The implications of these findings for investiga-tors using the semantic differential to assess themeaning of homogeneous concept domains areimportant. Specifically, the findings suggest



479

changes in the procedures investigators typicallyuse to select scales for inclusion in their instru-ment, to analyze their three-dimensional array,and to obtain attitude scores.

In the original use of the semantic differential(i.e., defining the basic dimensions of connota-tive meaning), it was reasonable to select largeheterogeneous groups of scales representingseveral meaning dimensions as well as a largeheterogeneous group of concepts and to assessthe dimensionality of the scales across concepts.In the analysis of a semantic differential specifi-cally devised to assess attitude within a given do-main, however, different strategies are required.Specifically, the concepts must be carefullyselected to represent the specific area of interest;it is neither necessary nor desirable to include a

variety of concepts if the investigator is in-terested in, for example, assessing attitude to-ward a particular organization. Further, thescales selected should meaningfully relate to theparticular set of concepts included. For ex-

ample, scales such as hot-cold, sweet-sour, andhigh-low do not seem to be as relevant when rat-ing the concept MYSELF as are scales such assuccessful-unsuccessful, important-unimpor-tant, or strong-weak. Still further, the dimen-sionality of this particular combination of scalesand concepts must be empirically determined;the evidence for concept-scale interaction is toostrong to naively assume that a scale which load-ed on the Evaluative dimension in previous re-search will necessarily represent the same

Evaluative dimension in a new study using a dif-ferent domain of concepts.

Finally, in determining this dimensionality,the concepts and scales should be &dquo;strung out&dquo;;that is, each concept-scale combination shouldbe treated as a separate variable. Such a pro-cedure allows for differences in responses from

concept to concept to become evident, ratherthan obscuring these differences by summing oraveraging responses. Stated another way, the

findings in this study suggest a series of &dquo;don’ts&dquo;for investigators; namely, (1) don’t presume themeaning of scales on the basis of previous re-

search ; (2) don’t sum across scales presumablyreflecting the same meaning dimension withoutevidence that the scales do, in fact, represent aunidimensional factor; (3) don’t sum across con-cepts within a concept domain unless there isevidence that the responses to the various con-

cepts are highly similar; and (4) don’t sumacross concepts reflecting different concept do-mains.

The ready interpretability of the two types offactors obtained as a result of treating each con-cept-scale combination as a variable deservesdiscussion here. As indicated earlier, several

procedures have been used by investigators toreduce semantic differential data to two dimen-sions. Curiously, the procedure used by Ku-biniec and Farr and in this research is atypical.Why is this the case? Surely summing or averag-ing across scales (within each of the meaning di-mensions) or across concepts potentially ob-scures useful information. A &dquo;stringing out&dquo; ap-proach, then, would seem to provide more com-prehensive data than a summing or averagingapproach.’ 7Of the three &dquo;stringing out&dquo; approaches, i.e.,

subjects x concepts, subjects x scales, and con-cepts x scales, the last approach would seem tobe the most useful and interpretable, at leastwhen the purpose is to assess attitude toward a

given concept domain. It appears to be an ob-vious extension of the usual approach to corre-lating data for a given group. That is, the usualdata matrix consists of subjects (rows) x items ortests (columns), leading to an item x item cor-relation matrix. The extension of this wouldseem to be to begin with a data matrix consistingof subjects (rows) x responses to specific com-binations of concepts and scales (columns), lead-ing to an item by item correlation matrix, whereitem is a, response to a particular scale whenused with a particular concept.

7 Use of the "stringing out" approach was recommended byMaguire (1973) in his review of semantic differential

methodology.



480

&dquo;Stringing out&dquo; concepts and scales seems tohave three desirable outcomes. First, it allowsthe investigator to directly observe the extent ofand nature of concept-scale interaction, ratherthan merely having to consider this phenomenonas a possible source of error. Second, the ap-proach provides the investigator with two typesof factors to interpret, i.e., factors within con-

cepts and factors across concepts. The two typesof factors may be extremely useful in either anheuristic or a predictive sense. Third, it providesinvestigators interested in analyzing their datathree-dimensionally with the first component oftheir analysis. Moreover, the three-mode analy-sis briefly summarized in this paper suggeststhat the single most useful component of thethree-mode factor analysis is, in fact, the factoranalysis of the concept-scale variables. Depend-ing on the investigator’s purpose, then, it mightbe sufficient to &dquo;string out&dquo; concepts and scalesand to analyze the data using the traditionaltwo-dimensional factor analysis rather than themore complex three-mode factor analysis.

In spite of these potential advantages, how-ever, the method of &dquo;stringing out&dquo; conceptsand scales is seldom used. Systematic studyshould be made of the relative advantages anddisadvantages of each of the &dquo;stringing out&dquo;

techniques.The similarity in structure found in this re-

search across two different semantic differen-tials raises interesting questions for further re-search. Further investigation of the psychologi-cal meaning of these two types of factors acrossvarious attitude domains is desirable. Such in-

vestigation would provide insight into attitudemeasurement using the semantic differential, aswell as allowing the multidimensionality of atti-tude to become evident and accounted for. Theexistence of the two types of factors appears tobe of heuristic value. The two types of factorsmay further prove to be of predictive value. Fu-ture research should investigate the predictive

validity of within-concept factors and across-concept factors.

References

Cattell, R. The meaning and strategic use of factoranalysis. In R. Cattell (Ed.), Handbook of multi-variate experimental psychology. Chicago: RandMcNally, 1966.

Farr, S. D., & Kubiniec, C. Stable and dynamic com-ponents of self-report self-concept. MultivariateBehavioral Research, 1972, 7, 147-163.

Gulliksen, H. How to make meaning more meaning-ful. Journal of Contemporary Psychology, 1958, 3,115-119.

Heise, D. Some methodological issues in semanticdifferential research. Psychological Bulletin, 1969,72, 406-422.

Jennrich, R., & Sampson, P. Rotation for simpleloadings. Psychometrika, 1966,31, 313-323.

Komorita, S., & Bass, A. Attitude differentiation andevaluative scales of the semantic differential. Jour-nal of Personality and Social Psychology, 1967, 6,241-244.

Kubiniec, C., & Farr, S. D. Concept-scale and con-cept-component interaction in the semantic differ-ential. Psychological Reports, 1971, 28, 531-541.

Maguire, T. O. Semantic differential methodologyfor the structuring of attitudes. American Educa-tional Research Journal, 1973, 10, 295-306.

Mayerberg, C. K., & Bean, A. G. The structure of at-titude toward quantitative concepts. MultivariateBehavioral Research, 1974, 9, 311-324.

Osgood, C., Suci, G., & Tannenbaum, P. Themeasurement of meaning. Urbana, IL: Universityof Illinois Press, 1957.

Tucker, L. R. Some mathematical notes on three-mode factor analysis. Psychometrika, 1966, 31,279-311.

AcknowledgmentsThe authors gratefully acknowledge Thomas O.

Maguire’s useful comments about our &dquo;stringingout&dquo; procedure.

Author’s Address

C. K. Mayerberg, 606 Ritter Annex, Temple Univer-sity, Philadelphia, PA 19122.



types the analysis of semantic differential attitude data

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